基于SSD的小目标检测改进算法

摘要/Abstract

摘要： 目标检测算法因数据存在分辨率较低、噪声等干扰，不能有效利用特征图中目标的边缘纹理和语义信息，导致小目标检测效果较差。为此，本文提出一种基于SSD的小目标检测改进算法。首先，采用普通卷积和深度可分离卷积进行同步特征学习并融合，获得信息丰富的浅层特征。然后，在固有的5个尺度的特征层后添加通道和空间自适应权重分配网络，使得模型更关注通道和空间的重要特征信息。最后，将候选目标框进行非极大抑制筛选得到检测结果。通过将改进的方法与Faster RCNN、SSD等方法在VOC2007数据集上测试结果进行比较，该方法降低了小目标的误检率，提升了整体目标的精度，所提模型mAP达到了78.94%，比SSD网络提高了3.13%。

关键词: 小目标检测, 深度可分离卷积, 多尺度, 权重分配网络, SSD模型

Abstract: Target detection algorithms cannot effectively use the edge texture and semantic information of small targets in the feature map due to low data resolution and noise interference, resulting in poor detection results. To solve this problem, this paper proposes an improved algorithm for small target detection based on SSD. Firstly, common convolution and deep separable convolution are used for synchronous feature learning and fusion, and the information-rich shallow features are obtained. Then the channel and space adaptive weight distribution network is added after the inherent 5 scale feature layer, so that the model pays more attention to the important feature information of the channel and space. Finally, the candidate target frame is subjected to non-maximum suppression screening to obtain the detection result. By comparing the improved method with Faster RCNN, SSD and other methods on the VOC2007 data set, the method reduces the false detection rate of small targets and improves the accuracy of the overall target. The proposed model mAP reaches 78.94%. It is 3.13% higher than the SSD model.

Key words: small target detection, depth separable convolution, multi-scale, weight distribution network, SSD

程凯强, 张旭, 寇旭鹏. 基于SSD的小目标检测改进算法[J]. 计算机与现代化, 2021, 0(07): 77-82.

CHENG Kai-qiang, ZHANG Xu, KOU Xu-peng. An Improved Algorithm for Small Target Detection Based on SSD[J]. Computer and Modernization, 2021, 0(07): 77-82.

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